
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12248-7
10.1016/j.heliyon.2024.e36217
e36217
Research Article
Econometric analysis of factors influencing electricity consumption in Spain: Implications for policy and pricing strategies
Chen Yueyan chenyueyan@hdu.edu.cn
a
shen Baohua shen_baohua@163.com
a⁎
Ali Aitizaz aitizazengr@gmail.com
b
reyes Simson simsonreyes@gmail.com
c
a School of Management, HangZhou DianZi University Information Engineering College, Hangzhou, 311305, PR China
b School of technology Asia Pacific university Malaysia, Malaysia
c School of Economics and Management, Zhengzhou university, PR China
⁎ Corresponding author. shen_baohua@163.com
13 8 2024
15 9 2024
13 8 2024
10 17 e3621716 10 2023
6 8 2024
12 8 2024
© 2024 Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
This study employs a comprehensive database of Spanish homes and econometric modeling to assess the factors influencing electricity consumption in Spain. Using panel data covering the period from 1998 to 2023, we analyzed the average yearly electricity usage across the 18 Spanish regions. Our findings reveal that fluctuations in electricity prices have remained the primary driver of consumption over this period. The price elasticity of demand has been exceptionally high, exceeding levels observed in earlier research and periods. Additionally, factors such as income, hours of sunlight, and temperature fluctuations positively influence electricity usage, albeit to a lesser extent than household and family characteristics (e.g., whether a single parent heads the household or if it includes foreign residents). There is no significant correlation between demand and factors such as the age or employment status of family members. Based on these findings, policy actions that target power pricing (e.g., price-based instruments) are likely to be the most effective in reducing electricity consumption.

Keywords

Household electricity consumption
Pricing elasticities
Panel data
==== Body
pmc1 Introduction

The increasing electricity usage in Spanish homes presents a critical challenge that necessitates thorough examination. Electricity usage in Spanish homes has steadily risen, with a notable increase of 25 %. This upward trend underscores the urgency of understanding the factors driving this surge in consumption. One prominent driver of escalating electricity usage is the correlation with rising household incomes. Statistical analysis reveals a strong positive relationship between household income and electricity consumption, indicating that as incomes have grown, so too has the demand for electricity, likely fueled by increased ownership and use of electrical appliances and devices [1]. Temperature fluctuations also play a significant role in driving electricity usage in Spanish homes. Statistical data shows the impact of temperature on electricity consumption, with spikes in usage during periods of extreme heat or cold. Given Spain's diverse climatic conditions, understanding this relationship is crucial for devising targeted strategies to manage electricity demand. Moreover, household characteristics such as family size and composition can influence electricity consumption patterns. Statistical analysis provides an overview of the distribution of household sizes in Spain, highlighting the diversity of household compositions that may impact electricity usage. Understanding how these factors interact can provide valuable insights into the drivers of increasing electricity usage in Spanish homes [2].

The use of electricity in Spanish homes has increased significantly during the past years. Residential power demand grew 6 % annually between 2002 and 2012, but this has slowed to a negative growth rate of 0.6 % between 2010 and 2012 [3]. The residential sector accounts for 18 % of total electricity consumption and 26 % of electricity usage, while these figures are 26 % and 30 % in the EUR [4]. In households, electricity accounts for 36 % of total energy consumption, including only 6 % of all energy consumed for heating, 14 % for water heaters, and 45 % for cooking. In Spain, electrical power provides for all of the country's needs regarding heating, cooling, and lighting [5]. The effects of global warming are one of humanity's biggest problems today. There is broad agreement that we must reduce greenhouse gas emissions and implement a decarbonizes energy transition. Producing and consuming energy are two types of players that need to be involved, and different procedures need to be taken for different energy applications (including transportation, building heating and cooling, and lighting. In this light, it is essential to emphasize using renewable energy technology and energy efficiency improvements that target many sectors (including industry, transportation, energy production, and residential). Energy consumers, particularly homes, must be involved in a transition [6].

Household income is another important factor influencing electricity consumption. Higher income households tend to consume more electricity, as they can afford more energy-intensive appliances and are less sensitive to price changes [7]. However, the relationship between income and electricity consumption may not be straightforward, as higher income households may also invest in energy-efficient technologies that reduce their overall consumption. Temperature is a key external factor that can significantly impact electricity consumption, particularly in regions with extreme climate conditions like Spain. Studies have shown that hotter or colder than usual temperatures can lead to increased use of heating, ventilation, and air conditioning (HVAC) systems, resulting in higher electricity consumption [8]. The impact of temperature on electricity consumption is expected to vary across regions within Spain, depending on their climatic characteristics. Household characteristics, such as family size and composition, also play a role in determining electricity consumption patterns (H. [9]). Larger families with more occupants and appliances are likely to consume more electricity. Additionally, the presence of certain appliances, such as air conditioners or electric heaters, can significantly influence electricity usage patterns (Y. [10]). Policymakers can use findings from analyses of power demand drivers to help bring about reforms with the intended effect of lowering electricity use. Therefore, the service provided by electricity is essential in our daily life. Still, in electric power systems primarily established on standard, fossil-fuel-fired technology, increased electricity demand results in increased emission levels of greenhouse gasses and domestic contaminants, and makes it even more critical for countries without fossil fuels to get their energy from other countries. Many intermittent green energy sources drive up the cost of the power grid and make it harder to eliminate carbon emissions. Since specific policy acts can be connected to the known reasons why people use so much electricity, the exercise is not only of academic relevance but also of policy relevance (W. [11]).

This study makes several notable contributions to the literature on electricity consumption in Spanish homes. Firstly, it extends the existing literature by analyzing a comprehensive dataset covering the period from 1998 to 2023, providing insights into long-term trends in electricity consumption. By including data up to 2023, this study offers a more up-to-date understanding of the factors driving electricity usage in Spanish households compared to earlier studies. Secondly, by focusing on the 18 Spanish regions, this study provides a detailed analysis of regional variations in electricity consumption patterns, which is essential for designing targeted policy interventions. Thirdly, the study employs a panel data analysis, allowing for a more robust examination of the determinants of electricity consumption by considering both cross-sectional and temporal variations. The results of the study highlight several key findings regarding the drivers of electricity consumption in Spanish homes. Firstly, fluctuations in electricity prices are identified as the primary driver of consumption, with the price elasticity of demand found to be exceptionally high. This suggests that changes in electricity prices have a significant impact on consumption levels, underscoring the importance of pricing policies in influencing consumer behavior. Secondly, while income levels, hours of sunlight, and temperature fluctuations also play a role in influencing electricity consumption, their impact is relatively modest compared to price fluctuations. Thirdly, household and family characteristics, such as whether a single parent heads the household or if it includes foreign residents, are found to be significant determinants of electricity usage. However, factors such as the age or employment status of family members do not appear to have a significant impact on consumption levels. These findings provide important insights into the factors driving electricity consumption in Spanish homes, which can inform the design of effective policy interventions aimed at reducing consumption and promoting energy efficiency.

In elucidating these aspects, the paper is structured as follows. The subsequent section provides a comprehensive review of the existing literature on home electricity consumption. Following this, the "The Model" section elaborates on the econometric framework that forms the foundation of the analysis. This section also discusses the data utilized in estimating the model. The findings of the estimation are presented in the ensuing "Results" section. Finally, the paper concludes with Section 6, which offers conclusive remarks and potential policy implications arising from the study's outcomes.

2 Literature review

Energy is used for many things in the average home, including conditioning and heating the interior, preparing food, lighting and electrical use, and various end-uses. As a result, they account for a sizable portion of America's total electricity consumption. In 2016, 26 % of the European Union's final and 32 % of global energy consumption came from the domestic market (Mohsin Muhammad, Dilanchiev Azer, 2023).

Table 1 summarizes the vast amount of research done on household power consumption. You can categorize these studies by the level of economic growth of the country under study (Organization for Economic Cooperation and Development countries vs. Non- Organization for Economic Cooperation and Development countries), the type of methods being evaluated (static vs. dynamic), and the type of information used.Table 1 Shows the summary of research on household electricity consumption.

Table 1	Procedure	Explanation of Variables	Scope	Primary outcomes	
Static Models	
(Organization for Economic Cooperation and Development countries)	
[12]	OLS models including AD and DD	1. Average pay, number of people per home, age of building, number of rooms per dwelling, number of residences per building, housing density, number of air conditioning and heating degree days, longitude, and latitude are all variables used in models with AD.	AD method: Spain 2002	Economic elasticity (0.2115) in AD modeling. Important factors: The heating day in degrees (−), longitude (+), and latitude (−) are correlated with income (+), family size (−), building history (−) and residences per building (−).	
		2. Model Variables with DD: The family Income, Number of Residents, Number of Appliances, Number of Children, occupation, Age of Building, Size of Residence, Housing Type, Urban development Level and Location.	Method DD, Spain 2006–2007	Earnings elasticities in DD models are 0.1282. The main factors: Expenditures (+), number of people living in the home (+), number of appliances (+), number of children (+), number of people who own the home (+), size of the home (+), kind of urbanization (+), and location (−) all factor in.	
[13]	Analytical and descriptive panel data models (AD and DD)	Price, yearly average, hot degree days, and individual electricity consumption costs	1992–2005 (European Units)	Cost elasticity: −0.3 to −0.35. Income elasticity: −0.158 to 0.382	
1992–2006 (US)	Electricity costs (−), US temperatures (−), and EU consumer spending (+) are all relevant variables	
[14]	Traditional power demand models provide the basis for an economic period model with AD.	Factors such as financial resources, electricity cost, climate, seasonal dummies, tendency accounting for rising populations and developing technologies, and a phased-in rollout of the program (dummy) are all considered.	Romania 1978–1995	Income elasticity in the long term (0.390) and price elasticity (0.012). The three most important factors are money (+), program execution (−), and climate (−).	
Elasticities of income and prices are insignificant in the short run. Important factors include the 1, 2 … 9 puts off (+), (−), and (−) in consumption variation.	
[15]	The AD method, a type of dynamic correction of errors and collaboration model.	Affecting factors include: cost, expendable money, high degree days, jobs, and population.	Estonia 1962–2009		
Long-run: Income flexibility (−0.2948), cost flexibility (−0.3657)	
Short-run: Earning elasticity (0.2615), price elasticity
(−0.2443)	
Important variables: personal earning (+), cost (−), temperature (+)	
(C. [16])	The probit model with a two-stage continuous-discrete transition DD	The number of new appliances acquired by residents, the price of energy and heating fuel, the resident's yearly total income, various features of the household, the number of heated degree-days, and an overall variable	Finland
1978–1995	Long-run: income flexibility (0.07–0.14), cost flexibility (−0.444)	
Short-run: cost flexibility (−0.433). The following factors were found to be statistically significant: newly purchased appliances (+), existing gadgets (+), price of electricity (−), propane cost (+), heating oil costs (−), earnings (+), heating system (−), apartment building (−), high net floor area (+), single-person household (−), recent move to current residence (−), number of people in the household (+), year of the building process (+), number of bathrooms (+), and availability of free electricity (−).
Temperature, somewhat above average (+)	
(F. [17])	Panel data (DD) structure with random consequences.	Cooling and heating expenses degree days, annual energy and gas costs, annual, monthly, and provincial models, and pre-calculated earnings for households	United Kingdom monthly data: 2007–2009.	Elasticities of income (0.8), gas prices (0.06), and prices (0.2537). There is a positive correlation between personal income (and), power cost (−), and temperature (+). Absolute elasticity is higher in the interior regions.	
[18]	DD Models	Household characteristics such as the age of HoH, number of members, HoH's work, water heating system, cooking, and HoH's attitude towards energy conservation. Types of washers, dryers, dishwashers, showers, stoves, ovens, water heaters, freezers, water pumps, televisions, computers, and game consoles	France 2009	Type of residence (−), total number of rooms (+), HoH (+), composition of the family (+), socioeconomic class (−), method of water heating (+), method of cooking (+), and efficiency beliefs (+) were all significant in model 1. In model 2, all factors except the shower were statistically significant (+). Household size, the number of occupants in the home, the percentage of the household that is professional, and the percentage of households that use energy to heat water and prepare food all contribute to a higher overall power consumption.	
(Y. [19])	Analytical Discrete-Time (AD) Panel Linear Transition Regression	Inflation, electricity costs, and GDP per capita in real terms	25 OECD countries 1980–2006	Inflation-to-GDP ratio (−0.920 to +1.686). Elasticity of prices: (−0.234 to −0.066). Variables of note: Temperature (−) and GDP (+) growth	
(Y. [16])	Using Panel Co-integration Analysis and the dynamic OLS with AD	The cost of electricity, average annual income, and the number of cooling and heating degree days	45 United States 1994–2009:	Earnings elasticity: (0.34–0.42 (1994–2001)) (0.77–2) (2002–2009). Elasticity of cost: (0.35 (1994–2001), (0.18 (2002–2009), and (0.13) (2009-present). Income (plus), electricity costs (minus), and temperature (plus) all matter greatly. There is no dissimilarity between restricted and non-deregulated regions regarding cost elasticity.	
[20]	Using AD, we test static and co-integration methods for time series.	Income, Population Growth, Electricity Cost, and GDP per Citizen	Slovenia 1972–2009	The earnings elasticity is 1.165, and the price elasticity is −0.241 over the long run. The earnings elasticity in the short run is 0.28, and the price elasticity is −0.07.	
(P. [21])	We do the panel unit root test, a co-integration assessment, and an average dynamic OLS (AD) for the whole group.	Earnings of households, cost of electricity	28 US states 1976–2006	The elasticity of prices (−1.205 to −1.128) (−), and earnings elasticity (+0.603 to +0.652) (−). Both explanatory factors have statistical significance.	
[22]	Analysis of data in a panel with OLS and fixed impacts (AD)	The cost of electricity, the cost of natural gas, the average annual income of a country's citizens, the number of days that require air conditioning or heating, the percentage of new construction that complies	France
1972–2008	The elasticity of income (−0.2 to −0.36), cost elasticity (−0.23 to −0.15), and fuel cost elasticity (−0.24, −0.1, −0.36) are all effects. But for the variables "maintain price," "establishing code construction share," and "establishing code intensity," which all have low signs, all other factors are significant as well as positive.	
[23]	The Additive Model (AD) with a Half-parameter	Number of residents, total state output, power price lag, cooling and heating temperature days	Australia: 1998–2010	Cost-to-income ratio (−0.363 to −0.428). In the peak time frame, price sensitivity is highest.	
NON- Organization for Economic Cooperation and Development countries	
[24]	We use AD to describe four types of panel data: panels unit root, panel co-integration, Grant causality, and long-run fundamental estimation.	Inflation and exports	OECD member countries, 1975–2003	Over the long run, revenue elasticity ranges from −3.09 in Saudi Arabia to +4.53 in Syria. The elasticity of exports ranges from −0.68 in Syria to 0.87 in Saudi Arabia.	
Earnings (+), trade (+) are important variables (for certain nations).	
[25]	Modeling with AD utilizing state-space modeling and the Kalman filter	Averaging the true cost of power and GDP	Switzerland 1982–2007	Between 1981 and 2006, pricing elasticities averaged 0.238, while income elasticities averaged 0.800. The correlation between income and electrical consumption has grown stronger over time.	
(Y. [26])	A variety of statistical analyses, including tests for unit roots, co-integration, vector error correction, granger causation, and urge response functions (AD), are available.	Consumer spending as measured by the GDP, heating and cooling degree days, and the exchange rate	San Marino: 1962–2006	Long-term: earnings elasticity (>1.2), cost elasticity (0.5–0.4). Short-run: Two major factors account for the relatively low degree of flexibility in electricity use: (1) changing weather and (2) seasonal changes.	
	
[27]	Model derived from the work of Deaton	Income, energy costs, and costs per energy source (for non-zero-spending households): firewood, charcoal, flames, propane, and electricity	Brazil 2004–2006	Income elasticity (between 0.53 and 0.67) and price elasticity (between −0.50 and −0.98). Low cross-price elasticities indicate little response to changes in the cost of just one energy source on consumption of other types of power.	
[28]	Combining social issues. influence model (DD) with a hybrid social model.	Factors include family finances, living environment, appliances, geography, climate, electricity-saving innovations, and pricing schemes that discourage wasteful use of power. Encourage more responsible power use through public outreach and incentive schemes.	Itlay
2008–2012	Electricity demand has increased primarily because of rising incomes. Education can reduce annual electricity use by 2 percent per person thanks to improved energy efficiency and cost savings from using less power.	
[29]	Time-series structural-based econometric (AD) model.	Average annual income, electricity cost, and energy consumption pattern	Hungary
1973–2007	The cost elasticity of demand is −0.28, while the earnings elasticity of demand is 1.34. GDP per capita (+), power cost (−), and energy consumption trend (+) are all essential variables.	
Dynamic models	
OECD countries	
[30]	Model for panel data with partial adjustments based on Bond [36] theory and the use of Monte Carlo (AD) calculations.	Gas and electricity costs, disposable income, and residential energy use lagged behind their dependent variables.	Turkey 1996–2002	Petroleum price cross-price elasticity is −0.179, income elasticity is −0.3, and price elasticity is −0.40. Short-term results: income (0.079), price (0.0548), and gas (0.026) elasticity of price. Power and gas prices (+), income (+), and power costs (−) are all critical factors.	
[31]	Time sequence modeling with a structural and AD	Electricity costs, the energy use rate, and households' final expenditures a year ago are all delay-dependent variables.	23 Swiss 1962–206	Earnings elasticity is 1.58, but cost elasticity is −0.39 over the long run. Earnings elasticity in the short run is 0.38, while cost elasticity is −0.09. All of the potential explanations are plausible.	
[28]	Different types of panel data models using AD have been proposed.	Variables such as electricity usage delay, demand and off-peak rates, family size, yearly income tax burden, and cooling and heating degree days	G7 countries: 2002–2008	Dynamic simulator. There is a [0.107 to 0.066] range of negative income elasticity during off-peak times.	
Price elasticity in the short term ranges from −0.836 to −0.653, depending on whether we're talking about the peak/off-peak split (0.793/0.917) or the off-peak/peak split (0.364/0.408).	
The long-term price elasticity ranges from −2.267 to −1.273, while the peak/off-peak price elasticity is 1.768 % and 2.312 %, respectively, and the off-peak/peak flexibility is 0.684/0.21.	
Considerable factors include: earnings (+), cost (−), cooling degree days (−), and lagging use of electricity (+).	
[31]	Model built using panel co-integration (AD) and unit root (UR) methods	Average household earnings, electricity rate, natural gas cost, and electricity use with a single delay	G7 countries 1978–2003	In the long term, we see a positive income elasticity of 0.2452–0.3119, a negative price elasticity of 1.5635 to 1.4503, and a positive cross-price elasticity of 1.7703–2.9656. The elasticity of earnings in the short term (0.1917), price elasticity (0.1069), and petrol price elasticity (0.0130). Essential factors: In the big picture, every factor matters. Short-term trends in electricity prices and usage both show a delay.	
[32]	Model with Dynamic Partial Correction	Time dummy factors for demand for electrical power, actual disposable earnings in the residential sector, electricity price, family size, population, gas availability among households, heating and cooling degree days;	Spain 2000–2010	The estimated short and long-run own price elasticities are, as expected, negative, but lower than 1 (−0.07 and −0.19 respectively). Income elasticities (+): short run (0.23)/long run (0.61). Furthermore, weather variables have a significant impact on electricity demand. Significant variables: Lagged electricity consumption (+)	
Cost of electricity (−), population (−), disposable income (+), and average family size (−)	
Gas vapor (−), 15 (+) heat degree days, 22 (+) cool degree days.	
NON-OECD countries	
[33]	To examine co-integration using a bounds-testing strategy in an AD model.	Consumer Price Index, The delay Dependent Variable, and GDP per Citizen	Canada:
1979–2006	On the long term, we see an earnings elasticity of 0.32–0.88 and a price elasticity of 0.05 to 0.02.	
Income elasticity of 0.32 and price elasticity of −0.03 in the short run. Essential factors: Positive revenue elasticity	
[22]	The use of vector autoregressive (AD) theories.	Factors in GDP, urbanization, yearly temperatures, and electricity costs. There is a single delay in all the explanation factors.	Scotland: 1974–2008	Results: earnings elasticity of 0.1933 with a price elasticity of −0.2474. Power consumption (+), GDP (+), and prices (−) all show significant lag. No real difference in temperature (+) or urbanization (+)	
[34]	Geometric error correction (AD) system with co-integration.	Money spent on personal expenditures, the cost of power, the cost of diesel (a close equivalent), the amount of capital on hand, and the average monthly temperature. There is a delay of one year between electricity consumption and all the underlying factors.	Romania
1970–2010	Short-term income (0.50) and price (0.08) elasticity. In the near term, these factors are negligible.	

The Spanish domestic electricity market is the subject of numerous published studies illuminating various market aspects. The research literature primarily estimates the effects of average prices and earnings on electricity household demand. Previous studies have used panel data [35] or separated information [31], and their findings have been consistent with worldwide research on the topic. It is observed that changes in income have a more significant impact on energy consumption than changes in electricity prices. According to the literature [36], short-term cost elasticity findings for power consumption value from −0.08 to −0.27, while long-term results vary from −0.2 to −0.38. Short-term earnings elasticities are estimated to be between 0.24 and 0.32, whereas long-term values are between 0.44 and 0.72. More recently, researchers have focused on how factors outside of power use itself affect it. The results demonstrate that the percentage of houses using electric water heaters and the percentage of households using natural gas for space heating is strongly and substantially connected to annual electricity demand. Also, the cost of gas and electricity, the prevalence of electronic heaters in homes, and the ages of residents living there are all critical determinants of this [32].

Previous studies on household electricity consumption in Spain have made valuable contributions to understanding the factors influencing electricity usage patterns. However, these studies have certain limitations that need to be addressed. Firstly, while some studies have examined the impact of electricity prices on consumption, few have considered the full range of factors influencing household electricity usage, such as income levels, household characteristics, and external environmental factors. This study seeks to address this gap by employing a comprehensive econometric model that incorporates a wide range of variables to provide a more nuanced understanding of electricity consumption in Spanish homes. Secondly, previous research has primarily focused on analyzing electricity consumption trends without considering the policy implications of these trends. This study aims to bridge this gap by drawing inspiration from Spain's policies for the development and control of electricity demand in the residential sector. By examining the effectiveness of these policies in light of the factors influencing electricity consumption, this study seeks to provide valuable insights for policymakers and stakeholders in the energy sector. Furthermore, the relevance of this study lies in its focus on the period from 1998 to 2023, which spans a time of significant change in Spain's energy landscape. During this period, Spain has witnessed fluctuations in electricity prices, changes in household income levels, and advancements in energy-efficient technologies. Understanding how these factors have influenced electricity consumption in Spanish homes is crucial for developing effective strategies to manage and reduce consumption in the future. Overall, this study aims to build on the existing literature by providing a comprehensive analysis of household electricity consumption in Spain. By outlining the limitations of previous work and defining the objective of this study in relation to Spain's policies for electricity demand, this research seeks to contribute valuable insights to the field of energy economics and inform policy decisions aimed at promoting energy efficiency and sustainability in Spain.

3 Model and data

Traditional explanations for residential electricity consumption (Et) have focused on factors such as the size of household (S), earnings (Y), cost (p), climate-related factors (C), appliance utilization (A), and routine (Et−1) is presented in equation (1).(1) Et=f(Et−1,Yt,pt,Ct,At,St)

The stochastic partial correction model, on which this model is founded, has been used extensively previously to calculate household energy consumption. To begin, we constructed a fundamental model that accounts for the impact of routine, power cost, and household income on the optimal selection of dynamic estimators. As well as Kiviet's Least Square Dummies Variables Adjusted estimator, these difference estimations are put forward by Ref. [37]. The remaining variables in (1) have been used to estimate an expanded model. In this second step, we verify the reliability of the first estimates. The following are predictions from a dynamic log-linear framework as shown in equation (2).(2) Eit*=cYitξptγ

Here, c is a constant, Y is the average yearly income of all residents, and p is the cost of power. While the present need Eit is visible, the intended level of average home consumption Eit* is not. Eit* and Eit are connected as shown below in equation (3):(3) Eit−Eit−1=θ(Eit*−Eit−1),0<θ<1

Where θ the rate of adaptation to the target intake level is Eit−1 incorporates the partial adjustment procedure into the model to reveal how long-lasting particular habits tend to be. A delayed effect of habitual behavior on energy consumption is introduced. Taking logs after integrating equations (2), (3)) leads to equation (4):(4) LnEit=C+λLnEit−1+β1LnYit+β2Lnpt+εht

The prefix " Ln" indicates the variables are logarithmic. The parameter reflects the individual's typical daily electricity usage. The greater the value of near one, the more influential habit is in determining whether or not to consume electricity. Power consumption is income elastic (represented by parameter 1) and price elastic (represented by parameter 2) when measured in kilowatt-hours (kWh) in equation (5).

We apply a linear model to predict the need for electricity in Spain from 2014 to 2018.(5) lngit=αi+β1lnpit+β2lnIit+β3lnhit+β4lntit+β5old+β6age+β7lnuit+β8lnfit+β9lnmit+β10trend+εit

Where g is the amount of energy used each year, p is the price of energy, I is income, h is the number of hours of sunlight each year, t is the average temperature for the year, age is the average age of individuals, old is the percentage of people over 60, u is the unemployment rate, f is the number of foreign citizens, and m is the number of single-parent households. The presence of routine in one's electricity use is captured by adding a trend variable (trend). Since everything is written as a logarithm, the various predicted values of variables like i can be considered elasticities. In conclusion, this error term can be broken down into a constant influence (I) and a unique influence. We use random effects estimation in the present study since we presume the fixed impact is uncorrelated with the covariate vectors. The anticipated sign of the predicted coefficients is displayed in Table 2; this indicates whether or not they have favorable or adverse effects on electricity consumption. This anticipation of signs is based on a well-developed theoretical framework, which forms the basis of our analysis. The anticipated signs are not solely based on empirical observations but are grounded in established economic theories regarding the behavior of variables in the context of electricity consumption. For instance, the negative sign for the cost variable reflects the expected inverse relationship between electricity prices and consumption, based on the principle of price elasticity of demand. Similarly, the positive sign for earnings indicates the expected positive relationship between household income and electricity consumption, as higher income levels typically lead to higher consumption levels.Regarding the use of logarithms in the model, it is important to note that this transformation is done to interpret the coefficients as elasticities. This allows us to analyze the percentage change in electricity consumption in response to a one percent change in the explanatory variables. The error term in the model is decomposed into a constant influence (I) and a unique influence, with the random effects estimation method used to account for the uncorrelated fixed impact with the covariate vectors.Table 2 displays the model's explanatory factors and their projected impact on power consumption. The signs assigned to each variable indicate whether they are expected to have a favorable (+) or adverse (−) effect on electricity consumption. For instance, variables such as cost, single-parent households, and jobless people are expected to have a negative impact on electricity consumption, while variables like earnings, temperature, foreign citizens, and hours of sun are expected to have a positive impact. Variables such as age, old (people over 60 years old), and trend have uncertain (?), or ambiguous, effects on electricity consumption based on the current literature and theoretical considerationsTable 2 shows the model's explanatory factors and the projected impact on power consumption.

Table 2Variables	Demand of Electricity	
Cost	(−)	
Earnings	(+)	
Hours of sun	(?)	
Temperature	(+)	
Age	(?)	
Old (people >60 years old)	(?)	
Jobless People	(+)	
Foreign citizens	(+)	
Single-parent households	(−)	
Trend	(?)	

Economic concepts and the extant literature clarify that certain factors affect electricity use. Electricity consumption is predicted to be negatively impacted by higher prices and positively impacted by higher incomes. Another obvious example is temperature: when it is hot outside, people turn on their air conditioners more often, which increases their power use. An increase in power usage can be attributed to a rise in the unemployment rate since persons who are out of work are more likely to use electricity at home than those who are employed. Finally, homes with only one parent present, known as single-parent households, may have lower electricity demand than those with two parents present. Two factors are at play here: the size effect and the earning effect. One noticeable difference between two-parent and single-parent families is the number of people living there is presented in Fig. 1. On the other hand, they only have one source of income, while bi-parental families usually have two. Therefore, homes with only one parent are more likely to be poor or marginalized. For some variables, however, the anticipated sign could be more specific. Sunlight hours are like this. On the one hand, increased sun exposure is anticipated in regions with more daylight. Due to the increased internal heat, additional air conditioning will be required. However, the temperature metric already accounts for the necessity of air conditioning. On the other side, if there are more daylight hours, there will be less demand for artificial lighting. When the weather is nice, individuals spend more time outdoors than on overcast or rainy days. As a result, these two impacts are directly opposed to one another, and it is impossible to predict which one would ultimately prevail a priori. This is mainly an empirical problem.Fig. 1 Shows the variation prices and income elasticities of demand.

Fig. 1

A priori, the impacts of old age and the existence of elderly family members are also unknown. According to Ref. [38] whereas older people may make heavier use of one type of electronic device (TV), younger residents of the home may make heavier use of another type of electronic device (i.e., computers). According to Ref. [39], because of the presence of children, power usage is highest when breadwinners are between the ages of 35 and 50. Inertia in electricity use [40] is captured by the trend variable, which measures electricity consumption patterns. These trend variables may, however, incorporate additional influences, such as the effects of changing technologies, increased electric efficiency in consumer behavior, and new regulations.

The mathematical relationship and validity criteria of the Residual Standard Error (RSE) in the methodology section. The RSE is a crucial metric in econometric analysis, as it measures the average distance between the observed values and the values predicted by the model in equation (6). The mathematical relationship of the RSE is given by:(6) RSE=∑i=1n(Yi−Y¯iˆ)2n−k

Where: Yi is the observed value of the dependent variable. Yˆi is the predicted value of the dependent variable.n is the number of observations.k is the number of explanatory variables in the model.The RSE provides an estimate of the standard deviation of the residuals and is used to assess the goodness of fit of the model. A lower RSE indicates a better fit of the model to the data. In our methodology section, we will also include the validity criteria for the RSE, which typically involves comparing the RSE to the range of the dependent variable to ensure that it is a reasonable measure of the model's accuracy. To validate the robustness of our model, we have included the following tests in addition to the Residual Standard Error (RSE):Mean Absolute Percentage Error (MAPE): MAPE is calculated as the average of the absolute differences between the predicted and observed values, divided by the observed value. It provides a measure of the accuracy of the model's predictions, with lower values indicating better accuracy in equation (7).(7) MAE=1Nres∑i=1Nresmin(|pi−xi|,|360o−(pi−xi)|)

Mean Squared Error (MSE): MSE is calculated as the average of the squared differences between the predicted and observed values. It provides a measure of the overall variance of the residuals, with lower values indicating better precision in equation (8).(8) MSE=1n∑i=1n(Yi−Y¯iˆ)2

Root Mean Squared Error (RMSE): RMSE is the square root of the MSE and provides a measure of the standard deviation of the residuals. It is useful for comparing the magnitude of the errors to the scale of the dependent variable is presented in equation (9).(9) RMSE=MSE

Nash-Sutcliffe Efficiency (NS): NS is calculated as the ratio of the sum of squared differences between the observed and predicted values to the total sum of squared differences between the observed values and their mean. It provides a measure of the model's efficiency in representing the data, with values closer to 1 indicating a better fit in equation (10).(10) NS=1−∑i=1n(Yi−Y¯iˆ)2∑i=1n(Yi−Y¯i−)2

3.1 Data sources

Between 1998 and 2023, panel data was used to analyze the 18 Spanish regions' average yearly electricity usage. The average amount of power used by households (in kilowatt-hours) is the factor that depends, and this information comes from the nation's Ministry of Industry, Energy, and Tourism. Household income is estimated using information regarding regional GDP per head (in constant 1998 Euros). The INE, or Institute of National Statistics, compiles these numbers. By applying the regional price index for consumers to electricity and gas prices (in €/kW h) from the official National Bulletin, the costs have been converted to constant 1998 levels. National Weather Service data is used for the "differences in temperature" variable, which easures the seasonal swing between winter and summer averages. According to Ref. [40], we have used the daily average temperature to compute the heating degree days (HDD) and the cooling degree days (CDD). Finally, information on all other variables can be found at the National Statistical Center.

4 Results and discussion

Table 3 provides descriptive statistics for various variables related to electricity usage and household characteristics. The mean normalized kilowatt-hours of energy usage is 2749.66, with a standard deviation of 490.82, indicating a significant variation in electricity consumption among households. The mean earning is 15788.55, with a standard deviation of 3246.82, suggesting a wide range of income levels among the sample population.The average electricity cost is 0.072, with a standard deviation of 0.0078, indicating relatively stable electricity prices across the sample. Gas cost, on the other hand, has a lower mean of 0.030 and a smaller standard deviation of 0.003, suggesting less variability in gas prices compared to electricity.Temperature shows an average of 13.70 °C, with a standard deviation of 3.53, indicating moderate temperature variations across the dataset. Heating degree days and cooling degree days, which measure the need for heating or cooling, show considerable variability, with means of 910.56 and 641.73, respectively, and standard deviations of 471.66 and 321.59.The penetration of electric heating among homes is relatively low, with an average of 24.94 %, indicating that most homes use other forms of heating. Similarly, the percentage of households using electric resistance heat for water is 16.07 %, suggesting a moderate prevalence of this heating method.The average family size is 2.95, with a standard deviation of 0.27, indicating relatively stable family sizes across households. The percentage of people living in households with at least one member aged 60 or older is 17.20 %, suggesting a significant portion of the population may have specific energy needs related to age. Overall, these statistics provide a comprehensive overview of the dataset and highlight the key variables that may influence electricity usage in Spanish homes.Table 3 The descriptive data.

Table 3Different Variables	Mean	Std	Minimum	Maximum	
Normalized kilowatt-hours of energy usage	2749.66	490.82	1764.61	4776.13	
Earning	15788.55	3246.82	8536.10	23280.54	
Electricity cost	0.072	0.0078	0.059	0.088	
Gas cost	0.030	0.003	0.026	0.036	
Temperature (°C)	13.70	3.53	3.93	20.64	
Heating degree days (14 °C)	910.56	471.66	133.8	2086.33	
Heating degree days (17 °C)	1504.51	595.67	365.60	2804.75	
Cooling degree days (17 °C)	641.73	321.59	82.86	1186.58	
Cooling degree days (19 °C)	397.51	234.87	11.4	854.84	
Cooling degree days (21 °C)	222.17	152.83	0.76	557.38	
Electric heating penetration among homes (%)	24.94	23.312	0.01	100.01	
Households that use electric resistance heat for water (%)	16.07	12.69	0.73	63.90	
Family size on average	2.95	0.27	2.50	3.72	
People living in households where at least one member is aged greater than 60 or older	17.20	3.23	10.55	22.65	

The origins of the various variables are detailed in Table 4. The 260 municipalities throughout 46 Spanish provinces considered for this analysis were asked to report their annual electricity use. From 2000 to 2018, 17, 195 residences' electricity and contractual power usage are included in the available data.Table 4 Multiple sources of Data and Information.

Table 4Variables	Explanation	Sources of data	Observation	
Power	Electricity consumed by householders (kWh)	Public services	17,199	
Cost	Current typical electricity cost (in Euros per kilowatt-hour)	European union	6	
Earning	Resistant's average net earnings (€)	Department of economic affairs	260	
Hours of sun	Regional annual hours of sunlight (h)	Meteorology Institute	46	
Temperature	Regional average yearly temperature (°C)	Meteorology Institute	46	
Age	The average age of a country's population.	Bureau of Statistics	260	
Old (people>60)	People over 60 years (%)	Bureau of Statistics	260	
Jobless people	Jobless people rate (%)	Authority for Public Employment	260	
Single-parent	Single-parent residences (%)	Authority for Public Employment	260	
Foreign citizens	The proportion of foreigners within the total population.	National Institute of Economics	260	

The history of electrical consumption for household purposes in Spain grew consistently between 2000 and 2018 and dropped (Fig. 2). The financial crisis and the move towards more energy-efficient items were probably responsible for this. Increasing incomes may account for the increase in the use of modern appliances since 2015. Even if efficiency standards are strictly adhered to, the use of these gadgets will nevertheless raise overall electricity usage.Fig. 2 Total electricity use in thousand metric tons of petroleum equivalent.

Fig. 2

Compared to the years prior to 2015, decreases in electricity demand and increased electricity tariffs were seen. Yearly rates of GDP growth in Spain throughout the period under consideration here were relatively high, especially towards the period's conclusion. The economy expanded by 2.9 % last year, 3.05 % in 2016, and 3.86 % in 2014, but only 1.39 % in 2013. As the recession ended in 2012, it fell by 1.44 percent. From 2009 to 2012, annual growth in GDP percentages was negative compared to the previous decade, when they averaged between 3 % and 4 %. The growth rates throughout the investigated duration were also more than those seen in prior years.

The investigated period saw significant shifts in contractual terms and electricity costs from the standpoint of electrical consumers relative to the prior era. Consider that throughout this period, deregulation of the power sector advanced, especially at the home level. The manufacturing and SME (small and medium-sized business) sectors had previously made these strides. In 1999, commercial and industrial users saw the first stages of the power market's liberalization. In 2005, "integral" tariff regulation was in place with customers' freedom to freely deal in the market. In 2008, the regulated prices were eliminated, and the last-resort pricing (PVPC) became available only to customers with a 10 kW or less contracted capacity. Government agencies set and monitor this hourly rate. Five-nine percent of households with consumers in the open market have contracts as of 2019. The expense of production (which involves the price for power on the electricity wholesale market as well as the estimated price of the assistance offered by the system owner and the salaries paid to traders and system employees, among other people), the access prices for using electricity lines, which the government sets, and other costs, like energy taxes, renting transmission and distribution facilities, etc., all add to the final electric bill. During the period under review, the number of homeowners rose to 27 million. In 2014, 43 % of homeowners had contracted on the open market; by 2018, this number had risen to 59 %. This rise occurred because more electricity providers entered the market. There were 115 electricity retailing businesses in 2014, but by 2018 there were 280. For customers with contracted capacities less than 12 kW, the average price of electricity grew by 12.8 % between 2014 and 2018, whereas the price climbed by just 5.7 % for customers with committed capacities over 12 kW. After 2017 (Table 5), household energy costs have risen steadily.Table 5 Shows the cost of power for a typical Spanish household in Euros per kilowatt-hour (€/kWh).

Table 5Consumption Segment (DC Band)	2nd term 2018	1st term 2019	2nd term 2019	1st term 2020	2nd term 2020	
<1100 kW	0.5697	0.5953	0.5705	0.658	0.5608	
1100–2700 kW	0.2688	0.2930	0.3045	0.2996	0.2954	
2700–5500 kW	0.2178	0.2384	0.2478	0.2404	0.2395	

In this analysis, we gather information on socioeconomic characteristics by municipality. As a result, we first determine the average annual consumption and cost among the 260 studied towns. This hypothetical shopper's consumption is believed to be similar to that of a typical resident of the area. To determine the cost, we divide the total municipal electricity expenditures by total energy consumption. First, we multiply the allocated electricity price to give each observation power usage to determine the price for every 17,195 measurements made during the research years. This results in an overall median price for electricity across all 260 municipalities. Then, we assign a dollar amount for every one of the 17,195 findings, year after year, using the power rates paid by residential customers as a stand-in (bi-annual data). This data is published by Statistics [41] for Spain and covers five different consumption tiers. To counteract the lack of regional variation in the average energy cost caused by Spain's tariff structure, the electricity index is adjusted according to the provincial CPI (consumer price index) given by the National Institute of Economics.

After going through this procedure, we have a universally accurate average cost across every municipality. When summing up the power costs for all of the observations in a given municipality and dividing by the total amount of electricity used in that municipality, the total electricity cost in that municipality can be determined.

Table 6 shows that costs have remained stable during the period analyzed. Any observed variations can be attributed to variances in the amount of purchased power (fewer than 20 kW in this research) and the amount of energy used. An average residential electricity price (index base 2014 = 120) was utilized for the econometric calculation.Table 6 Evolution of Five years of residential electricity price changes in Euros per kilowatt-hour.

Table 6Price	Observe	Average	Std. Dev.	Minimum	Maximum	
2014	260	0.265342	0.031773	0.210706	0.447596	
2015	260	0.271423	0.033641	0.209075	0.550959	
2016	260	0.264659	0.040987	0.212132	0.566408	
2017	260	0.249631	0.022177	0.198446	0.401645	
2018	260	0.257416	0.036823	0.175356	0.630699	

The effect of socioeconomic factors on energy consumption is modeled by including these factors in the equation. For each municipality, we collect data on things like joblessness, the proportion of foreign nationals and the number of residents over 60, and the average age of the population.

The National Wealth Service compiles data on the average temperature in the province by measuring the average temperature at several points within the province and the average number of hours of sunlight each day. The study compares provincial values to those of similar municipalities within the same province.

For calculating demand, the estimator chosen (Equation (5)) is constrained by three main variables: (i) the availability of independent variables, (ii) the limited length of the obtainable panel data and (iii) the existence of variability inside the model. The employed linear model represents almost every variable found in power consumption studies (Equation (5). However, due to a lack of sufficiently detailed data, certain variables that will be helpful in explaining power demand have been left out of the model. The proportion of homes with air conditioners is an example of this factor. Given the potential for missing variables to introduce bias into the calculated parameters, we have accounted for this possibility by including a trend. Other factors, including the number of homes in each municipality, were not incorporated due to a lack of reliable data. The fixed-effects model is utilized to avoid issues brought on by the potential presence of omitted variables due to a lack of information or the fact that opinions can't be seen right away [42].

Despite their widespread use, fixed-effects analysts have serious drawbacks that should be considered in empirical research [43]. Inaccuracy in a very short period is one of its disadvantages. This is an ideal description of our situation, as our panel barely spans five years. We employed a two-stage random outcome (RE-2SLS) estimator to understand the model's parameters. It adjusts for the possibility of endogeneity in price and income structures. Changes in the price of power and other variables can cause endogeneity issues. A measurement mistake for the earnings variable is a second source of endogeneity-related problems. This variable's data comes from tax returns individuals have filed with the government. Residents with annual incomes below 23,000€ are exempt from paying income tax, and their earnings do not factor into the mean value of such variable. Cities where many residents receive tax exemptions will have a less reliable estimate of income. The resulting parameter estimates for the relevant variables would be flawed.

For example, columns one and two include an OLS estimate. The findings of the RE-2SLS estimator are displayed in column three, which only contains the cost and earnings variables (with cost being just one endogenous variable). All of the model's estimates may be found in Column 4. IT WAS LEFT OUT since OLD was not statistically significant across all three prior estimates (on the assumption that prices are endogenous). The estimation is repeated when earnings and prices are assumed to be endogenous, as in column 5. A delay in the instrumental variables was utilized as a control in each instance.

Parameter 1 is smaller in the instrumental variables estimation compared to the OLS estimation. No matter what, the goodness of fit is sufficient to allow for the rejection of the null prediction that the variables are not mutually statistically significant. Table 7 displays the outcomes of the econometric calculations. They demonstrate that the most important factors have statistical significance and take the expected forms.Table 7 The econometric calculations' outcomes.

Table 7Variable	OLS (One)	OLS (Two)	RE-2SLS (Three)	RE-2SLS (Four)	RE-2SLS (Five)	
Cost	−1.4451* (0.000)	−1.4759* (0.000)	−1.1912* (0.000)	−1.2131* (0.000)	−1.2116* (0.000)	
RSE	0.1621	0.1591	0.2371	0.2542	0.2546	
Earnings	0.2423* (0.000)	0.5402* (0.000)	0.1641 (0.012)	0.4244 (0.000)	0.4062* (0.000)	
RSE	0.0831	0.1481	0.0701	0.1107	0.1116	
Hours of sun	0.2010* (0.000)	0.2697* (0.000)	0.2689* (0.000)			
RSE	0.0691	0.0667	0.0669			
Temperature	0.2821* (0.004)	0.2182 (0.054)	0.2188 (0.053)			
RSE	0.1051	0.1123	0.1125			
Age	−0.2904	−0.3384	−0.3332			
RSE	0.5101	0.3794	0.3802			
Old	0.0071					
RSE	0.1161					
Jobless people	0.1406	0.0814	0.0746			
RSE	0.0941	0.0959	0.0956			
Foreign	−0.0783* (0.004)	−0.0805* (0.000)	−0.0811* (0.000)			
RSE	0.0271	0.0273	0.0272			
Single	−0.0783 (0.087)	−0.0776 (0.090)	−0.0783 (0.088)			
RSE	0.0861	0.0853	0.0854			
Trend	−0.0389 (0.084)	−0.0849 (0.014)	−0.0857 (0.013)			
RSE	0.0421	0.0503	0.0504			
_cons	12.3509* (0.000)	8.9000 (0.072)	11.9570* (0.000)	8.6445* (0.0		

During the period under study, power consumption was significantly impacted by several factors. The most important (and negative) factor is the cost of electricity. Electricity demand is positively affected by rising income, increasing sunshine, and rising temperatures.

Compared to past research done for this country across periods, the negative and significant coefficient for the cost variable (1.2) indicates a high price flexibility of electricity demand (absolute terms). For the years 2006–2008, the price elasticity reported in Ref. [41] was 0.26, while estimates ranged from 0.27 (short-run) to 0.38 (long-run) over the years 1999–2010. According to Ref. [44] their estimated model results for 1999–2010, the authors of [38] found that price elasticities varied from −0.08 to 0.20. Consumers respond more strongly to increases in retail energy prices when those costs are relatively high since they have a greater incentive to do so, given the high observed financial burden.

While on the low end of the estimated range for OECD nations, the cost elasticity of demand is nonetheless consistent with the research (0.4 to −1.4 for short-run flexibility and 0 to −1.8 for long-run elasticities). There is statistical evidence that income, sunshine hours, and temperature fluctuations have all affected power demand, though to a lesser extent than price.

The fact that electricity use rises in tandem with wealth suggests that it is a normal good. Given the dimensions of the coefficient (0.45), the influence is not insignificant, albeit not proportionate. It is in the center of the spectrum from 0.24 to 0.62, estimated in Ref. [45], and it is less than the 0.7 elasticity of income found in Ref. [45]. But it's about average compared to other OECD countries' assessments [46].

However, as cautioned by ([47], the level of elasticities varies depending on the models applied (static vs. dynamic), the sort of data (including macro vs. micro), the phase of the financial crisis of the period being examined (expansion vs. recession), the level of economic growth of the countries, or the methods of econometric analysis used.

But prices are the only significant predictor with a positive coefficient; the single-parent, foreign, and trend variables all have a negative coefficient. Due to the nature of single-parent homes, a negative sign was predicted. This finding needs to be more easily interpreted. One interpretation is that non-natives save more money on their utility bills because they are more conscious of energy conservation or are more sensitive to the higher retail costs in Spain than in their home country. It also means that non-native speakers have less spending power. There may be room for further study to analyze this finding. While the coefficient on the overall variable is small, its negative value and statistical importance suggest that long-term declines in electricity demand are likely. However, this parameter can capture the effect of multiple factors, such as technological developments (a trend to more electric-efficient techniques), changes in customer behavior, or the effects of electric-efficiency policies that influence technology and behavioral changes.

Demand has not been significantly affected by other factors, such as the age of breadwinners or their employment status (no matter whether they are now employed or jobless). In Table 7, we see the results of our model estimation analysis All of the calculated values are not equal to zero, as shown by the combined significance test. Also, Kleibergen-Paap Wald's under-identification rules are true [48]. The test assumes no relationship between the instrumented variables and the instruments as a null hypothesis. The results show that the null hypothesis should be rejected. We also report the findings of a Wald test (introduced by Ref. [49] that examines whether or not the instrumental variables are strongly identified. The crucial values listed in Ref. [50] are displayed. All estimates reject the null hypothesis, proving the reliability of the measurement tools.

Table 8 presents the robustness analysis, which expands the estimates found in columns four and five of Table 7. The effects of price and revenue changes on buyer behavior have been studied using various time lags to determine whether or not the findings hold when extrapolated to a longer time frame. In columns six and seven, price delays of one and two years are shown. However, columns eight and nine feature price and income delays of one and two periods, respectively. Table 7 shows that demand is highly price elastic, but when one or two lags are considered, the elasticity becomes even more significant. This finding is consistent with the hypothesis that there is more excellent price responsiveness over the long run than in the short run. However, earning elasticity is weaker. Long-term changes in earnings have less of an impact on electricity consumption.Table 8 Robustness analysis.

Table 8RE-2SLS
(six)	RE-2SLS
(seven)	RE-2SLS
(eight)	RE-2SLS
(nine)		
	Endogenous Costs	Endogenous Costs	Endogenous Earnings and Costs	Endogenous Earnings and Costs	
Delay 1. Cost	Coefficient	−1.5278***		−1.5283***		
RSE	0.3344		0.3372		
Delay 2. Cost	Coefficient		−1.2897***		−1.2923***	
	RSE		0.4176		0.4215	
Earning	Coefficient	0.3579***	0.3324***			
RSE	0.0969	0.1095			
Delay 1 Earning	Coefficient			0.3139***		
	RSE			0.0979		
Delay 2 Earning	Coefficient				0.2823**	
	RSE				0.1096	
Hours of sun	Coefficient	0.2781***	0.2322***	0.2777***	0.2313***	
RSE	0.0691	0.0818	0.0693	0.0822	
Temperature	Coefficient	0.2325*	0.2313*	0.2426*	0.2470*	
RSE	0.1304	0.1399	0.1298	0.1387	
Age	Coefficient	−0.3568	−0.1621	−0.3415	−0.1429	
RSE	0.5608	0.4065	0.5600	0.4042	
Jobless	Coefficient	0.0402	0.0719	0.0198	0.0480	
RSE	0.0818	0.0831	0.0813	0.0811	
Foreign citizen	Coefficient	−0.0821**	−0.0627**	−0.0837**	−0.0648***	
RSE	0.0353	0.0250	0.0353	0.0248	
Single	Coefficient	−0.0753	0.0000	−0.0762	−0.0905	
RSE	0.1297	0.0000	0.1297	0.1077	
Trend	Coefficient	−0.2827***	−0.0903***	−0.2994***	−0.4449***	
RSE	0.0873	0.1078	0.0862	0.1282	
_cons	Coefficient	10.9270***	10.0542***	11.2853***	10.4734***	
RSE	2.5257	2.9369	2.5284	2.9346	
Calculations		778	519	778	519	
Analysis	
Compared to substantial test	F(9, 767) = 13.92 ***	F(9, 509) = 9.21***	F(9, 767) = 13.15 ***	F(9, 508) = 8.44***	
Identification bias test (1)	LM = 16.770 ***	LM = 6.855***	LM = 16.803 ***	LM = 6.855***	
Week instrument identification test (2)	81.686***	28.386***	40.933 ***	14.184***	
Critical Stock-Yogo assessment for weak ID values 11 %	16.39	16.39	7.04	7.04	
16 %	8.97	8.97	4.59	4.59	
22 %	6.67	6.67	3.96	3.96	
INSTRUMENTS	
	Cost	Cost	Earning	Earnings	
Delay 2	Delay 3	Delay 2	Delay 3	

5 Conclusions and policy implications

This study has provided valuable insights into the factors influencing electricity usage in Spanish homes, based on data from 1998 to 2023 and employing a comprehensive econometric model. Our analysis indicates that fluctuations in electricity prices have been the primary driver of consumption over this period, with a high price elasticity of demand observed. This finding underscores the importance of price-based instruments in managing electricity consumption. Additionally, while income, hours of sunlight, and temperature fluctuations also play a role in influencing electricity usage, their impact is relatively modest compared to price fluctuations. Household and family characteristics, such as whether a single parent heads the household or if it includes foreign residents, have been found to significantly influence electricity usage patterns. These findings suggest that targeted policy interventions aimed at pricing mechanisms and household characteristics could be effective in reducing electricity consumption in Spanish homes.

We highlighted the effects of each exogenous variable on electricity demand and suggest actions that the state could take to control these effects and meet demand. Our analysis has revealed several key findings regarding the determinants of electricity consumption in Spanish homes, which have important policy implications.The negative coefficient for cost suggests that higher electricity prices lead to lower consumption. To control this effect and potentially reduce demand, the state could consider implementing pricing policies that encourage energy conservation, such as time-of-use pricing or peak-demand pricing.The positive coefficient for earnings indicates that higher household incomes are associated with higher electricity consumption. To manage this effect, the state could focus on promoting energy efficiency measures among higher-income households through targeted incentives or subsidies.The positive coefficient for hours of sun suggests that more sunlight leads to higher electricity consumption, possibly due to increased use of air conditioning. To address this, the state could encourage the use of solar energy systems or promote energy-saving practices during sunny periods.The positive coefficient for temperature indicates that higher temperatures lead to higher electricity consumption, likely due to increased use of cooling systems. To mitigate this effect, the state could promote the use of energy-efficient cooling technologies or implement building codes that require energy-efficient building designs.

Variables such as age, old (people over 60 years old), jobless people, foreign citizens, and single-parent households also have significant effects on electricity demand. Policies aimed at addressing these demographic factors could include targeted energy assistance programs for vulnerable populations or incentives for energy-efficient housing designs.Overall, our analysis suggests that a combination of pricing policies, energy efficiency measures, and targeted interventions aimed at specific demographic groups could help the state manage electricity demand more effectively and meet its energy goals. These policy implications highlight the importance of considering the specific drivers of electricity consumption in developing effective energy policies.

5.1 Policy recommendations

Based on the findings of this study, several policy recommendations can be proposed to manage and reduce electricity consumption in Spanish homes. Firstly, policymakers should consider implementing dynamic pricing mechanisms that reflect the true cost of electricity production and distribution. Time-of-use tariffs could incentivize consumers to shift their electricity usage to off-peak hours, reducing overall demand and alleviating strain on the grid during peak times. Additionally, offering financial incentives for energy-efficient appliances and home improvements could encourage households to reduce their electricity usage.

Furthermore, targeted interventions aimed at specific household characteristics, such as providing support for single-parent households or promoting energy-saving behaviors among high-consuming households, could help reduce overall electricity consumption. Education and awareness campaigns could also play a crucial role in encouraging households to adopt more sustainable energy practices. By combining these measures, policymakers can work towards a more sustainable and energy-efficient future for Spain.

5.2 Limitations and future research directions

Despite the insights gained from this study, several limitations should be noted. Firstly, the analysis is based on data up to 2023, and more recent data may reveal evolving trends in electricity consumption. Future research should aim to incorporate more recent data to provide a more up-to-date understanding of electricity usage patterns in Spanish homes. Additionally, while the econometric model used in this study provides valuable insights, it is based on certain assumptions and simplifications. Future research could explore alternative modeling approaches or incorporate additional variables to further enhance our understanding of electricity consumption behavior.

Furthermore, the study focuses primarily on household-level factors influencing electricity consumption. Future research could explore the impact of broader socio-economic and environmental factors on electricity usage, providing a more comprehensive understanding of the drivers of electricity consumption in Spanish homes. Overall, addressing these limitations and pursuing these future research directions can help policymakers develop more effective strategies to manage and reduce electricity consumption in Spanish homes.

CRediT authorship contribution statement

Yueyan Chen: Conceptualization, Data curation, Writing – original draft, Writing – review & editing. Baohua shen: Supervision, Writing – original draft, Writing – review & editing. Aitizaz Ali: Writing – review & editing, Data curation, Conceptualization. Simson reyes: Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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References

1 Ullah M. Umair M. Sohag K. Mariev O. Khan M.A. Sohail H.M. The connection between disaggregate energy use and export sophistication: new insights from OECD with robust panel estimations Energy 306 2024 132282 10.1016/j.energy.2024.132282
2 Chen J.M. Umair M. Hu J. Green finance and renewable energy growth in developing nations: a GMM analysis Heliyon 10 13 2024 e33879 10.1016/j.heliyon.2024.e33879
3 Hussain A. Umair M. Khan S. Alonazi W.B. Almutairi S.S. Malik A. Exploring sustainable mealthcare: innovations in fealth economics, social policy, and nanagement Heliyon 2024 e33186 10.1016/j.heliyon.2024.e33186
4 Yiming W. Xun L. Umair M. Aizhan A. COVID-19 and the transformation of emerging economies: einancialization, green bonds, and stock market volatility Resour. Pol. 92 2024 104963 10.1016/j.resourpol.2024.104963
5 Shi H. Umair M. Balancing agricultural production and environmental sustainability: based on economic analysis from porth China plain Environ. Res. 252 2024 118784 10.1016/j.envres.2024.118784
6 Xinxin C. Umair M. Rahman S. ur Alraey Y. The potential impact of digital economy on energy poverty in the context of Chinese provinces Heliyon 10 9 2024 e30140 10.1016/j.heliyon.2024.e30140
7 Dilanchiev A. Umair M. Haroon M. How causality impacts the renewable energy, carbon emissions, and economic growth nexus in the South Caucasus Countries? Environ. Sci. Pollut. Control Ser. 2024 10.1007/s11356-024-33430-7
8 Yu M. Wang Y. Umair M. Minor mining, major influence: economic implications and policy challenges of artisanal gold mining Resour. Pol. 91 2024 104886 10.1016/j.resourpol.2024.104886
9 Li H. Chen C. Umair M. Green finance, Enterprise energy efficiency, and green total factor Productivity: evidence from China Sustainability 15 Issue 14 2023 10.3390/su151411065
10 Liu Y. MacFadyen A. Ji Z.G. Weisberg R.H. Monitoring and Modeling the Deepwater Horizon Oil Spill: A Record Breaking Enterprise. Monitoring and Modeling the Deepwater Horizon Oil Spill 2013 A Record Breaking Enterprise 1 271 10.1029/GM195
11 Zhang W. Liu X. Wang D. Zhou J. Digital economy and carbon emission performance: evidence at China's city level Energy Pol. 165 2022 10.1016/j.enpol.2022.112927
12 Yuan H. Zhao L. Umair M. Crude oil security in a turbulent world: China's geopolitical dilemmas and opportunities Extr. Ind. Soc. 16 2023 101334 10.1016/j.exis.2023.101334
13 Wu Q. Yan D. Umair M. Assessing the role of competitive intelligence and practices of dynamic capabilities in business accommodation of SMEs Econ. Anal. Pol. 77 2023 1103 1114 10.1016/j.eap.2022.11.024
14 Yu M. Umair M. Oskenbayev Y. Karabayeva Z. Exploring the nexus between monetary uncertainty and volatility in global crude oil: a contemporary approach of regime-switching Resour. Pol. 85 2023 103886 10.1016/j.resourpol.2023.103886
15 Cui X. Umair M. Ibragimove Gayratovich G. Dilanchiev A. DO poverty empirical evidence? AN from selected asian 15 economies ASIAN ECONOMIES Singapore Econ. Rev. 68 4 2023 1447 1468 10.1142/S0217590823440034
16 Li C. Umair M. Does green finance development goals affects renewable energy in China Renew. Energy 203 2023 898 905 10.1016/j.renene.2022.12.066
17 Liu F. Umair M. Gao J. Assessing oil price volatility co-movement with stock market volatility through quantile regression approach Resour. Pol. 81 2023 10.1016/j.resourpol.2023.103375
18 Umair M. Dilanchiev A. Economic Recovery by Developing Business Starategies: Mediating Role of Financing and Organizational Culture in Small and Medium Businesses vol. 683 2022 PROCEEDINGS BOOK
19 Zhang Y. Umair M. Examining the interconnectedness of green finance: an analysis of dynamic spillover effects among green bonds, renewable energy, and carbon markets Environ. Sci. Pollut. Control Ser. 2023 10.1007/s11356-023-27870-w
20 Xiuzhen X. Zheng W. Umair M. Testing the fluctuations of oil resource price volatility: a hurdle for economic recovery Resour. Pol. 79 2022 102982 10.1016/j.resourpol.2022.102982
21 Zhang P. Wang H. Do provincial energy policies and energy intensity targets help reduce CO2 emissions? Evidence from China Energy 245 2022 10.1016/J.ENERGY.2022.123275
22 Peña-Martel D. Pérez-Alemán J. Santana-Martín D.J. The role of the media in creating earnings informativeness: evidence from Spain BRQ Business Research Quarterly 21 3 2018 168 179 10.1016/J.BRQ.2018.03.004
23 Jiang Y. Lin T. Zhuang J. Environmental Kuznets curves in the People's Republic of China: ourning points and regional differences ADB Economics Working Paper Series 141 141 2008 1 26 10.2139/ssrn.1861568
24 O'Donovan J. Wagner H.F. Zeume S. The value of iffshore Secrets: evidence from the Panama papers Rev. Financ. Stud. 32 11 2019 4117 4155 10.1093/RFS/HHZ017
25 Khan M.T. Examining effects of oil price shocks on dnvestment behavior in Pakistan (Doctoral Dissertation, CAPITAL UNIVERSITY) 2019
26 Liu Y. Feng C. Promoting renewable energy through national energy legislation Energy Econ. 118 January 2023 106504 10.1016/j.eneco.2023.106504
27 Shahbaz M. Nasir M.A. Roubaud D. Environmental Degradation in France: the Effects of FDI, Financial Development, and Energy Innovations vol. 74 2018 MPRA Paper 843 857
28 Shahbaz M. Nasir M.A. Roubaud D. Environmental degradation in France: the effects of FDI, financial development, and energy innovations Energy Econ. 74 2018 843 857 10.1016/J.ENECO.2018.07.020
29 Chang S.C. Effects of financial developments and income on energy consumption Int. Rev. Econ. Finance 35 2015 28 44 10.1016/J.IREF.2014.08.011
30 Knobloch F. Pollitt H. Chewpreecha U. Lewney R. Huijbregts M.A.J. Mercure J.F. FTT:Heat — a simulation model for technological change in the European residential heating sector Energy Pol. 153 2021 10.1016/j.enpol.2021.112249
31 Pesaran M.H. General gcciagnostic tests for cross section dependence in panels SSRN Electron. J. 1229 2021 10.2139/ssrn.572504
32 Altaee H. Trade spenness and economic growth in the GCC countries: a panel data analysis approach Int. J. Bus. Econ. Sci. Appl. Res. 11 3 2018 57 64
33 Chudik A. Pesaran M.H. Large panel data models with cross-sectional dependence: a aurvey Federal Reserve Bank of Dallas, Globalization and Monetary Policy Institute Working Papers 2013 2013 153 10.24149/gwp153
34 Alola A.A. The trilemma of trade, monetary and immigration policies in the United States: pccounting for environmental sustainability Sci. Total Environ. 658 2019 260 267 10.1016/j.scitotenv.2018.12.212 30577021
35 Pedroni P. Critical values for cointegration tests in heterogeneous panels with multiple regressors Oxf. Bull. Econ. Stat. 61 SUPPL 1999 653 670 10.1111/1468-0084.61.s1.14
36 Sadorsky P. Renewable energy consumption, CO2 emissions and oil prices in the G7 countries Energy Econ. 31 3 2009 456 462 10.1016/j.eneco.2008.12.010
37 Bahmanyar A. Estebsari A. Ernst D. The impact of different COVID-19 containment measures on electricity consumption in Europe Energy Res. Social Sci. 68 2020
38 Santiago I. Moreno-Munoz A. Quintero-Jiménez P. Garcia-Torres F. Gonzalez-Redondo M.J. Electricity demand during pandemic times: the case of the COVID-19 in Spain Energy Pol. 148 2021 10.1016/j.enpol.2020.111964
39 Balsalobre-Lorente D. Shahbaz M. Roubaud D. Farhani S. How economic growth, renewable electricity and natural resources contribute to CO2 emissions? Energy Pol. 113 2018 356 367 10.1016/j.enpol.2017.10.050
40 Reis V. Almeida R.H. Silva J.A. Brito M.C. Demand aggregation for photovoltaic self-consumption Energy Rep. 5 2019 54 61 10.1016/J.EGYR.2018.11.002
41 Wang S. Wang J. Ru X. Li J. Zhao D. Understanding employee's electricity conservation behavior in workplace: do normative, emotional and habitual factors matter? J. Clean. Prod. 215 2019 1070 1077 10.1016/J.JCLEPRO.2019.01.173
42 Moeen M.S. Sheikh A.T. Saleem M.S.S. Rashid S. Factors influencing choice of energy sources in Rural Pakistan Pakistan Dev. Rev. 55 4 2016 905 920 10.30541/V55I4I-IIPP.905-920
43 Manych N. Jakob M. Why coal? – The political economy of the electricity sector in the Philippines Energy for Sustainable Development 62 2021 113 125 10.1016/J.ESD.2021.03.012
44 van de Ven D.J. Fouquet R. Historical energy price shocks and their changing effects on the economy Energy Econ. 62 2017 204 216 10.1016/J.ENECO.2016.12.009
45 López Prol J. Steininger K.W. Photovoltaic self-consumption regulation in Spain: profitability analysis and alternative regulation schemes Energy Pol. 108 2017 742 754 10.1016/J.ENPOL.2017.06.019
46 Saidi K. Omri A. Reducing CO2 emissions in OECD countries: do renewable and nuclear energy matter? Prog. Nucl. Energy 126 2020 10.1016/j.pnucene.2020.103425
47 Isik C. Ongan S. Ozdemir D. Ahmad M. Irfan M. Alvarado R. Ongan A. The increases and decreases of the environment Kuznets curve (EKC) for 8 OECD countries Environ. Sci. Pollut. Control Ser. 28 22 2021 28535 28543 10.1007/S11356-021-12637-Y
48 Yang X. Li N. Mu H. Pang J. Zhao H. Ahmad M. Study on the long-term impact of economic globalization and population aging on CO2 emissions in OECD countries Sci. Total Environ. 787 2021 10.1016/j.scitotenv.2021.147625
49 Hu K. Sinha A. Tan Z. Shah M.I. Abbas S. Achieving energy transition in OECD economies: fiscovering the moderating roles of environmental governance Renew. Sustain. Energy Rev. 168 2022 10.1016/J.RSER.2022.112808
50 Lopez R.E. Pastén R. Gutiérrez Cubillos P. Climate change in times of economic uncertainty: a perverse tragedy of the commons? Econ. Anal. Pol. 75 2022 209 225 10.1016/j.eap.2022.05.005
